The malignancy of hepatocellular carcinoma (HCC) is of great significance to prognosis. Recently, deep feature in the arterial phase of Contrast-enhanced MR has been shown to be superior to texture features for malignancy characterization of HCCs. However, only arterial phase was used for deep feature extraction, ignoring the impact of other phases in Contrast-enhanced MR for malignancy characterization. In this work, we design a discriminative multimodal deep feature fusion framework to both extract correlation and separation of deep features between Contrast-enhanced MR images for malignancy characterization of HCC, which outperforms the simply concatenation and the recently proposed deep correlation model.
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